This article reviews index structures for fast similarity search for objects represented by
binary vectors (with components equal to 0 or 1). Structures for both exact and approximate …

The proliferation of mobile devices is producing a new wave of applications that enable
users to sense their surroundings with smart phones. People are preferring mobile devices …

The emerging deep learning techniques prefer map** complex data objects (eg, images,
documents) to compact binary vectors (ie, hash codes) for efficient similarity search. In this …

A similarity search in Hamming space finds binary vectors whose Hamming distances are no
more than a threshold from a query vector. It is a fundamental problem in many applications …

A distance search in Hamming space finds binary vectors whose Hamming distances are no
more than a threshold from a query vector. It is a fundamental problem in many applications …

We present ElasticHash, a novel approach for high-quality, efficient, and large-scale
semantic image similarity search. It is based on a deep hashing model to learn hash codes …

Abstract Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data
processing and analytics, particularly for analyzing multimedia contents which are often of …

To overcome the barrier of storage and computation, the hashing technique has been widely
used for nearest neighbor search in multimedia retrieval applications recently. Particularly …

In this article, we propose a method of approximate asymmetric nearest-neighbor search for
binary embedding codes. The asymmetric distance takes advantage of less information loss …

Similarity-preserving hashing is a widely used method for nearest neighbor search in large-
scale image retrieval tasks. Considerable research has been conducted on deep-network …